Multi-Agent Architectures
Why split work across multiple agents
A single agent juggling many responsibilities (research, writing, fact-checking, formatting) often does each poorly. Splitting into specialized sub-agents — each with a narrow role and tailored system prompt — improves quality on each piece.
Common patterns: orchestrator and workers
An orchestrator agent breaks a goal into subtasks and delegates each to a specialized worker agent, then synthesizes their outputs into a final result. This mirrors how a human manager delegates to a team.
The real cost of multi-agent complexity
More agents means more API calls, more cost, more latency, and more places for coordination to fail. Only split into multiple agents when a single agent genuinely struggles — don't default to multi-agent for simple tasks.
Key Takeaways
- Specialized sub-agents each improve quality on their narrow responsibility.
- The orchestrator-worker pattern mirrors human team delegation.
- Multi-agent systems cost more in API calls, latency, and coordination overhead.
- Only split into multiple agents when a single agent genuinely underperforms.
Design a 2-agent split
Take a task a single agent struggles with (e.g., research + write + fact-check) and design a 2-agent split with clear responsibilities for each.